Gradient based method for symmetric and asymmetric multiagent reinforcement learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2690, Issue , 2004, Pages 68-75

  Könönen, Ville ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; ENGINEERING EDUCATION; MULTI AGENT SYSTEMS;

GRADIENT-BASED METHOD; INFORMATION STATE; LEARNING TASKS; MULTI-AGENT REINFORCEMENT LEARNING; NATURAL BASIS; PARAMETRIC FUNCTIONS; UTILITY VALUES; VALUE FUNCTIONS;

REINFORCEMENT LEARNING;

EID: 35048865922     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Article

References (4)

1. L. Baird and A. Moore. Gradient descent for general reinforcement learning. In M. Kearns, S. Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems, volume 11, Cambridge, MA, USA, 1999. MIT Press.
2. R. W. Cottle, R. E. Stone, and J.-S. Pang. The Linear Complementarity Problem. Academic Press, New York, USA, 1992.
3. J. A. Filar and K. Vrieze. Competitive Markov Decision Processes. Springer-Verlag, New York, NY, USA, 1997.
4. J. Hu and M. P. Wellman. Multiagent reinforcement learning: Theoretical frame-work and an algorithm. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML'98), Madison, Wisconsin, USA, July 1998. Morgan Kaufmann Publishers.